Beamforming, which is sometimes referred to as spatial filtering, is a signal processing technique used in sensor arrays for directional signal transmission or reception. For example, beamforming is a common task in array signal processing, including diverse fields such as for acoustics, communications, sonar, radar, astronomy, seismology, and medical imaging. A plurality of spatially-separated sensors, collectively referred to as a sensor array, can be employed for sampling wave fields. Signal processing of the sensor data allows for spatial filtering, which facilitates a better extraction of a desired source signal in a particular direction and suppression of unwanted interference signals from other directions. For example, sensor data can be combined in such a way that signals arriving from particular angles experience constructive interference while others experience destructive interference. The improvement of the sensor array compared with reception from an omnidirectional sensor is known as the gain (or loss). The pattern of constructive and destructive interference may be referred to as a weighting pattern, or beampattern.
As one example, microphone arrays are known in the field of acoustics. A microphone array has advantages over a conventional unidirectional microphone. By processing the outputs of several microphones in an array with a beamforming process, a microphone array enables picking up acoustic signals dependent on their direction of propagation. In particular, sound arriving from a small range of directions can be emphasized while sound coming from other directions is attenuated. For this reason, beamforming with microphone arrays is also referred to as spatial filtering. Such a capability enables the recovery of speech in noisy environments and is useful in areas such as telephony, teleconferencing, video conferencing, and hearing aids.
Signal processing of the sensor data of a beamformer may involve processing the signal of each sensor with a filter weight and adding the filtered sensor data. This is known as a filter-and-sum beamformer. Such filtering may be implemented in the time domain. The filtering of sensor data can also be implemented in the frequency domain by multiplying the sensor data with known weights for each frequency, and computing the sum of the weighted sensor data.
Altering the filter weights applied to the sensor data can be used to alter the spatial filtering properties of the beamformer. For example, filter weights for a beamformer can be chosen based on a desired look direction, which is a direction for which a waveform detected by the sensor array from a direction other than the look direction is suppressed relative to a waveform detected by the sensor array from the look direction.
The desired look direction may not necessarily be known. For example, a microphone array may be used to acquire an audio input signal comprising speech of a user. In this example, the desired look direction may be in the direction of the user. Selecting a beam signal with a look direction in the direction of the user likely would have a stronger speech signal than a beam signal with a look direction in any other direction, thereby facilitating better speech recognition. However, the direction of the user may not be known. Furthermore, even if the direction of the user is known at a given time, the direction of the user may quickly change as the user moves in relation to the sensor array, as the sensor array moves in relation to the user, or as the room and environment acoustics change.